Image(filename="images/pydata_logo.png", width=600)
BeautifulSoupIFrame('http://jamesbvaughan.com/python-twilio-scraping/', width=1024, height=600)
Image('images/jupytercon.png', width=1000, height=600)
IFrame(src="http://docs.pachyderm.io/en/latest/getting_started/beginner_tutorial.html", width=1024, height=600)
Image('images/review-committee.png', width=1024, height=600)
O'Reilly's data science salary survey, females made up 21(ish)% of respondents
%matplotlib inline
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(10, 8))
sns.set_style("whitegrid")
speaker_data = [
{
'year': 2014,
'percent_female': 0.075,
},
{
'year': 2015,
'percent_female': 0.1961,
},
{
'year': 2016,
'percent_female': 0.1538,
},
{
'year': 2017,
'percent_female': 0.1983,
}
]
df = pd.DataFrame(data=speaker_data)
# annotation
ax = sns.barplot(x="year", y="percent_female", data=df)
ax.set_ylim(0,0.25)
ax.set_title("Percent of female speakers at the PyData Conference")
# plot Strata female attendance rate
plt.plot(np.linspace(-1,4,100), [0.21]*100, 'b')
[<matplotlib.lines.Line2D at 0x118eba8d0>]
Image("images/fullfact.png", width=1024, height=600)
Data for good: Lessons from the frontline
Emma Prest, General Manager
Image("images/datakind.png", width=1024, height=600)
Picasso's terminal; data science and AI in the visual arts
Image("images/genekogan.png", width=1024, height=600)
Linky linky